Overview and management of post-intensive care syndrome
Bibliographic record
Abstract
Any patient suffering from critical life-threatening illnesses in most cases require hospitalization, in the care unit (ICU) where they can receive essential life-sustaining treatments. This has created an impact including more than 50 million individuals world-wide. Although advancements, in technology and healthcare have increased survival rates many individuals who survive these illnesses experience long-term impairments known as post-intensive care syndrome (PICS). PICS encompasses cognitive and emotional challenges that significantly affect patients' quality of life and ability to return to their normal routines. Caregivers may also face similar emotional hurdles, a condition referred to as PICS-family (PICS-F). The prevalence of PICS varies but can affect up to 50% of ICU survivors. Cognitive difficulties can be noticed in, around 70% of instances impacting abilities like memory, focus, and decision-making. These difficulties can lead to emotions such, as sadness worry, and a condition known as traumatic stress disorder (PTSD). Multiple factors, such as delirium, sedation, and pre-existing health conditions play a role, in the emergence and severity of PICS. Diagnosing PICS involves comprehensive assessments covering physical, cognitive, and emotional dimensions. Screening should ideally commence during the ICU stay and continue post-discharge. Assessment tools such as the Montreal cognitive assessment (MoCA) and emotional functioning screenings aid in identifying PICS. This manifests physically through muscle weakness and fatigue, impacting mobility and daily activities. Effectively managing ICUs requires the implementation of models and strategies that optimize resource utilization. However, these strategies may entail challenges such as data integration and stakeholder involvement. Preventing PICS involves proactive measures like reducing sedation, promoting early mobility, and offering rehabilitation services. Addressing PICS necessitates a proactive approach, comprehensive patient care, and collaboration among multidisciplinary teams. The successful implementation of these strategies depends on thorough evaluation and active engagement with all stakeholders involved in ICU management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".